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Pedotransfer functions (PTFs) based on easily measured soil variables offer an alternative for labor‐intensive bulk density (ρb) measurements. The predictive quality of 12 published PTFs was evaluated using an independent dataset of forest soils (1614 samples) from Flanders, Belgium. For all samples, PTF accuracy and precision was calculated, and for topsoil and subsoil samples separately. All functions were found to produce a systematic underestimation of predicted ρb, with mean prediction errors (MPEs) ranging between −0.01 and −0.51 Mg m−3 Most PTFs performed differently when applied to topsoil or subsoil data. Prediction of topsoil ρb showed the highest prediction error. The evaluation demonstrated the poor performance of some published PTFs, and raised concern that the predictive ability of even the better models may not be adequate. Therefore, two candidate PTFs were recalibrated and validated. With recalibration, accuracy improved considerably and showed a near‐zero bias, but precision increased only slightly. The best fitted empirical model was based on loss‐on‐ignition (LOI): ρb = 1.775 − 0.173(LOI)1/2 Its predictive capacity was not significantly better than the Adams physical two‐component model ρb = 100/{(LOI/0.312) + [(100 − LOI)/1.661]}. For the prediction of ρb in forest soils, LOI was two times more important than texture variables, and LOI alone accounted for >55% of the total variation. The lowest root mean squared prediction error (RMSPE) was 0.16 Mg m−3 for LOI‐based, and 0.21 Mg m−3 for texture‐based models. Separate calibration of topsoil and subsoil layers did not enhance the predictive capacity significantly.

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The soil moisture retention curve (MRC) is time consuming and expensive to measure directly. Several attempts have been made to establish a relation between readily available soil properties, like particle‐size distribution, organic matter content, and bulk density, and the soil moisture retention curve. Those relationships are referred to as pedotransfer functions (PTFs). The objective of this study was to evaluate some PTFs with respect to their accuracy in predicting the soil moisture retention curve. Five widely used and four more recently developed PTFs were selected for evaluation. Seven of the selected PTFs predict moisture retention function parameters, whereas the other two predict the moisture content at certain matric potentials. In order to quantify the prediction accuracy, the mean of the absolute value of mean differences (MAMD), the mean and the standard deviation of the root of mean squared differences (MRMSD and SDRMSD, respectively), and the mean of the Pearson correlation coefficient (Mr) were used. The evaluated PTFs were finally ranked based on these validation indices. The PTFs showed good to poor prediction accuracy with MAMD values ranging from 0.0312 to 0.0603 m3 m−3 and with MRMSDs between 0.0412 and 0.0774 m3 m−3 The SDRMSDs and Mrs ranged from 0.0212 to 0.0349 m3 m−3, and from 0.9468 to 0.9980, respectively. The validation indices computed by the PTF of Vereecken and coworkers gave the best results. Moreover, it predicts moisture retention function parameters, and therefore, this PTF is recommended most to predict the moisture retention curve from readily available soil properties.

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